Vivity AI recognized that shipyards and factories struggle with safety risks, aging equipment, and inefficiencies, while traditional monitoring is often slow and unreliable.
Ultralytics YOLO models empowered Vivity AI to create real-time industrial automation solutions with 99.8% object detection accuracy, fewer false alerts, and enhanced safety.
Industrial automation is crucial for improving efficiency and safety in workplaces like shipyards, petrochemical plants, and manufacturing facilities. However, traditional monitoring systems are often outdated, leading to costly downtime and safety risks. Vivity AI recognized these challenges and set out to develop a Vision AI-driven solution to enhance real-time monitoring, streamline workflows, and prevent operational failures.
To optimize industrial monitoring, Vivity AI developed computer vision solutions using Ultralytics YOLO models to enable real-time industrial automation. For example, their industrial AI innovations include automated defect detection, predictive maintenance, and workflow optimization, helping industries track production progress, detect faults before failures occur, and ensure compliance with safety regulations.
Manufacturing industries regularly face challenges related to automation and safety monitoring. For instance, in shipyards alone, over 1,000 subcontractors and 20,000 workers contribute to building a single ship. Manually monitoring these large operations often causes delays in spotting hazards, increasing the risk of accidents and costly downtime.
Vivity AI, a leading provider of AI-driven solutions for manufacturing, built computer vision solutions using Ultralytics YOLO models to meet these challenges. By leveraging computer vision tasks like object detection, Vivity AI’s innovations can identify hazards, track workers and equipment, and ensure safety compliance, improving efficiency and reducing risks in high-stakes environments.
When it comes to industrial settings like shipyards and heavy metal production factories, digital transformation can be complicated. Unlike straight-line manufacturing, these industries involve dynamic, non-linear workflows, making automation difficult to implement and sustain.
Some of the biggest concerns are health, safety, and environmental risks (HSE). Shipyards and factories often involve heavy machinery and large moving parts, making accidents more likely. Without effective real-time monitoring, safety incidents can lead to severe disruptions and workplace hazards.
Also, aging infrastructure and outdated processes can cause frequent equipment failures, quality control issues, and environmental compliance challenges. Many manufacturing plants still rely on manual oversight, which can be inefficient and prone to errors.
Meanwhile, another growing concern is the loss of skilled workers. As older generations retire, their experience and knowledge are not always passed down. This talent gap makes it harder to maintain efficient operations.
Putting these concerns together, Vivity AI recognized the need for a smarter, AI-driven approach to tackle these challenges and bridge the gap in automation, safety, and workforce knowledge retention.
To facilitate real-time industrial monitoring, Vivity AI integrated Ultralytics YOLO models into its AI-driven solutions. By automating time-consuming manual processes using computer vision tasks, these solutions help improve efficiency, reliability, and workplace safety.
Vivity AI’s key solutions include:
Vivity AI chose Ultralytics YOLO models for their high accuracy, fast processing, and efficient training compared to other Vision AI models. YOLO enables real-time object detection, which is critical for industrial automation, where delays can be expensive.
Unlike traditional models that need longer training times and higher computational power, YOLO provided Vivity AI’s solutions with 99.8% detection accuracy and low latency. Its ability to handle complex visual data from environments like shipyards and heavy manufacturing facilities made it an ideal choice for scalable, real-time industrial monitoring and safety compliance.
Vivity AI's computer vision solutions, built using Ultralytics YOLO, have made a significant impact across multiple industries.
For instance, in shipbuilding, their solution Dynamic Visual Intelligence (DVI) has improved efficiency by enabling faster block location determination, saving $2 million annually. Also, by optimizing ship assembly tracking, it has contributed to $3 million in yearly savings.
Similarly, in manufacturing and petrochemical industries, the Vivity Edge Platform has prevented revenue losses of nearly $2 million per unplanned shutdown, with additional productivity gains of $300,000 upon plant-wide deployment.
Achieving close to 100% accuracy in failure detection and maintaining false alarm rates below 0.1%, Vivity Edge provides streamlined monitoring and instantaneous warning notifications, making it possible for technicians to proactively address potential issues and minimize downtime.
Ultimately, with real-time inferencing at 60 FPS (frames per second), Vivity AI’s YOLO-powered solutions are making industrial operations more reliable and productive.
Looking ahead, Vivity AI is committed to further improving operational efficiency, safety, and environmental compliance for their global partners. By continuing to leverage advanced computer vision and machine learning technologies, they aim to help businesses in the chemical, energy, and manufacturing industries meet their digital transformation goals.
As they refine and expand their AI-powered solutions, Vivity AI remains focused on driving innovation and supporting industrial automation and optimization using tools like the Ultralytics YOLO models.
Interested in how Vision AI could enhance your business? Explore our GitHub repository to see how Ultralytics’ AI solutions are transforming innovations like AI in healthcare and computer vision in manufacturing. Learn more about our YOLO models and license options and take the first step toward smarter automation today.
Ultralytics YOLO models are computer vision architectures developed to analyze visual data from images and video inputs. These models can be trained for tasks including Object detection, classification, pose estimation, tracking and instance segmentation.Ultralytics YOLO models include:
Ultralytics YOLO11 is the latest version of our Computer Vision models. Just like its previous versions, it supports all computer vision tasks that the Vision AI community has come to love about YOLOv8. The new YOLO11, however, comes with greater performance and accuracy, making it a powerful tool and the perfect ally for real-world industry challenges.
The model you choose to use depends on your specific project requirements. It's key to take into account factors like performance, accuracy, and deployment needs. Here's a quick overview:
Ultralytics YOLO repositories, such as YOLOv5 and YOLO11, are distributed under the AGPL-3.0 License by default. This OSI-approved license is designed for students, researchers, and enthusiasts, promoting open collaboration and requiring that any software using AGPL-3.0 components also be open-sourced. While this ensures transparency and fosters innovation, it may not align with commercial use cases.
If your project involves embedding Ultralytics software and AI models into commercial products or services and you wish to bypass the open-source requirements of AGPL-3.0, an Enterprise License is ideal.
Benefits of the Enterprise License include:
To ensure seamless integration and avoid AGPL-3.0 constraints, request an Ultralytics Enterprise License using the form provided. Our team will assist you in tailoring the license to your specific needs.
.webp)
.webp)
.webp)